A general, algebraic equation for predicting total respiratory tract deposition of micrometer-sized aerosol particles in humans

Finlay, Warren H.

doi:10.1016/j.jaerosci.2006.11.002

Cited by 29 publications

(15 citation statements)

References 24 publications

(70 reference statements)

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“…The difference in reference FRC between male Caucasians and female Chinese, for instance, is 53% (see Table 1) and justifies our concern about categorization of lung morphology for different populations. Moreover, the FRC has been shown to have a large influence on aerosol deposition patterns Hofmann et al 2002), substantiating the importance of the morphology categorization process we have developed, which allows morphometric data (height) and respiratory tract characteristics (FRC) to be incorporated directly in the lung model, instead of being used to modify the results afterward (Martin and Finlay 2006).…”

Section: Discussionmentioning

confidence: 59%

“…This is based on the work of Hughes et al (1972) who had shown a very close correlation between changes of the cube root of lung volume and both the bronchial length and diameter. Although this study was performed on excised dog lungs, it has been used as a starting point for lung scaling in humans (Segal et al 2002;Martin and Finlay 2006). Consequently, to accommodate FRC variation, all airway diameters and lengths (hence the airway volumes) are modified by a uniform FRC scaling factor (SF 9-23 ) that is defined as the cube root of the ratio between the population's FRC (FRC pop ) and the Soong FRC (FRC Soong ):…”

Section: Differentiation Of Lung Morphologymentioning

confidence: 99%

“…They are based on the description of the deposition efficiency of particles by 3 main mechanisms: diffusion, sedimentation, and inertial impaction. Other models are based on empirical correlations (Martin and Finlay 2006;Finlay and Martin 2008) or use a semiempirical approach to describe the values of regional deposition (ICRP 1994). A third group of models uses the stochastic equations to predict the deposition of polydisperse aerosol within a symmetric structure of the human lung (Martonen 1993) or monodisperse aerosol in an asymmetric lung structure (Anjilvel and Asgharian 1995;Asgharian et al 2001).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Categorization of Lung Morphology Based on FRC and Height: Computer Simulations of Aerosol Deposition

Pichelin

Caillibotte

Katz

et al. 2012

Aerosol Science and Technology

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The aim of this study was to compare the human subject experimental measurements of particle deposition within the lungs using the aerosol bolus technique with the results of analytical modeling as a basis for assessing the influence of lung morphology on inhaled particle deposition patterns. A methodology for scaling the lung morphology, based on a classic symmetric dichotomous model, as a function of both functional residual capacity and height of the investigated population is presented. Because of the availability of deposition data for male and female lung morphologies, these were used as an example to address the importance of adjusting lung morphology in calculating the aerosol deposition rates. In order to represent the 2 groups based on gender enrolled in the experimental study, 2 lung morphologies have been built. An analytical and mechanistic model was used to mimic the bolus delivery technique and simulate the aerosol deposition in each of the 2 groups. Predicted results were compared with experimental data for both total deposition fraction and bolus recovery (fraction of exhaled particles compared with inhaled particles) for 3 flow rates and 3 particle sizes. Good agreement was found between theoretical and measured data, showing the primary importance of the differentiation of the lung morphology to predict the aerosol deposition within human lungs. This study presents a morphological lung model that is adaptable to specific populations (e.g., gender or race), groups (e.g., a clinical study population), or even individuals.

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Section: Discussionmentioning

confidence: 59%

Section: Differentiation Of Lung Morphologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Categorization of Lung Morphology Based on FRC and Height: Computer Simulations of Aerosol Deposition

Pichelin

Caillibotte

Katz

et al. 2012

Aerosol Science and Technology

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“…Finlay and Martin (10) recently reviewed historical semi-empirical models and new developments in predicting aerosol deposition in the respiratory tract. The whole-lung semi-empirical model developed by Martin and Finlay (11) was shown to match whole-lung in vivo deposition for ambient aerosols to a high degree. Additional corrections are required to account for jet effects associated with inhaler mouthpieces (12).…”

Section: Introductionmentioning

confidence: 99%

Validating CFD Predictions of Pharmaceutical Aerosol Deposition with In Vivo Data

et al. 2015

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Purpose CFD provides a powerful approach to evaluate the deposition of pharmaceutical aerosols; however, previous studies have not compared CFD results of deposition throughout the lungs with in vivo data. Methods The in vivo datasets selected for comparison with CFD predictions included fast and slow clearance of monodisperse aerosols as well as 2D gamma scintigraphy measurements for a dry powder inhaler (DPI) and softmist inhaler (SMI). The CFD model included the inhaler, a characteristic model of the mouth-throat (MT) and upper tracheobronchial (TB) airways, stochastic individual pathways (SIPs) representing the remaining TB region, and recent CFD-based correlations to predict pharmaceutical aerosol deposition in the alveolar airways. Results For the monodisperse aerosol, CFD predictions of total lung deposition agreed with in vivo data providing a percent relative error of 6% averaged across aerosol sizes of 1-7μm. With the DPI and SMI, deposition was evaluated in the MT, central airways (bifurcations B1-B7), and intermediate plus peripheral airways (B8 through alveoli). Across these regions, CFD predictions produced an average relative error <10% for each inhaler. Conclusions CFD simulations with the SIP modeling approach were shown to accurately predict regional deposition throughout the lungs for multiple aerosol types and different in vivo assessment methods.

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“…Existing semi-empirical models have been shown to predict whole-airway deposition of pharmaceutical aerosols accurately. (2,9,10) However, predicting regional and local aerosol deposition throughout the airways may require more advanced modeling techniques. (11,12) It is expected that regional and local deposition patterns of drugs in the lungs will influence efficacy and treatment success.…”

Section: Introductionmentioning

confidence: 99%

Validating Whole-Airway CFD Predictions of DPI Aerosol Deposition at Multiple Flow Rates

Longest

Tian

Khajeh-Hosseini-Dalasm

et al. 2016

Journal of Aerosol Medicine and Pulmonary Drug Delivery

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Background: The objective of this study was to compare aerosol deposition predictions of a new whole-airway CFD model with available in vivo data for a dry powder inhaler (DPI) considered across multiple inhalation waveforms, which affect both the particle size distribution (PSD) and particle deposition. Methods: The Novolizer DPI with a budesonide formulation was selected based on the availability of 2D gamma scintigraphy data in humans for three different well-defined inhalation waveforms. Initial in vitro cascade impaction experiments were conducted at multiple constant (square-wave) particle sizing flow rates to characterize PSDs. The whole-airway CFD modeling approach implemented the experimentally determined PSDs at the point of aerosol formation in the inhaler. Complete characteristic airway geometries for an adult were evaluated through the lobar bronchi, followed by stochastic individual pathway (SIP) approximations through the tracheobronchial region and new acinar moving wall models of the alveolar region. Results: It was determined that the PSD used for each inhalation waveform should be based on a constant particle sizing flow rate equal to the average of the inhalation waveform's peak inspiratory flow rate (PIFR) and mean flow rate [i.e., AVG(PIFR, Mean)]. Using this technique, agreement with the in vivo data was acceptable with <15% relative differences averaged across the three regions considered for all inhalation waveforms. Defining a peripheral to central deposition ratio (P/C) based on alveolar and tracheobronchial compartments, respectively, large flow-rate-dependent differences were observed, which were not evident in the original 2D in vivo data. Conclusions: The agreement between the CFD predictions and in vivo data was dependent on accurate initial estimates of the PSD, emphasizing the need for a combination in vitro-in silico approach. Furthermore, use of the AVG(PIFR, Mean) value was identified as a potentially useful method for characterizing a DPI aerosol at a constant flow rate.

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A general, algebraic equation for predicting total respiratory tract deposition of micrometer-sized aerosol particles in humans

Cited by 29 publications

References 24 publications

Categorization of Lung Morphology Based on FRC and Height: Computer Simulations of Aerosol Deposition

Categorization of Lung Morphology Based on FRC and Height: Computer Simulations of Aerosol Deposition

Validating CFD Predictions of Pharmaceutical Aerosol Deposition with In Vivo Data

Validating Whole-Airway CFD Predictions of DPI Aerosol Deposition at Multiple Flow Rates

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